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2021 Emerging Technology in computing, Communication and Electronics, ETCCE 2021

作者： Yet, Ong Zu Rassem, Taha H. Rahman, Md Arafatur Rahman, M.M. Universiti Malaysia Pahang College of Computing and Applied Sciences Faculty of Computing Pekan Malaysia Bournemouth University Faculty of Science and Technology PooleBH12 5BB United Kingdom University of Wolverhampton School of Mathematics and Computer Science WolverhamptonWV1 1LY United Kingdom Universiti Malaysia Pahang College of Engineering Department of Mechanical Engineering Kuantan26300 Malaysia

ISBN: (纸本)9781665483643

Fine-grained classification is a challenging problem as one has to deal with a similar class of objects but with various types of variations. For more elaboration, they are almost similar and have subtle differences, and are confusing. In this study, aircraft will be the fine-grained object to be focused on. Aircraft which has almost similar shapes and patterns can be hardly recognized even for humans, especially those who haven not gone through any training. In recent years, a lot of proposed methods addressed to solve the difficulties in fine-grained problems by learning contrastive clues from an image. This study aims to increase the accuracy of the Attentive Pairwise Interaction Network (API-Net) by introducing data augmentation into the network structure. Some of the previous studies proved that data augmentation does help improve a network. So, this study is going to modify the API-Net with different data augmentation settings. In this study, various settings have been introduced to the API-Net. Several experiments had been done with a simple modification where a portion of the train dataset's images will randomly convert into greyscale images. These settings are, only brightness & contrast 0.2, only grayscale 0.3, only grayscale 0.5, brightness & contrast 0.2 with grayscale 0.3, and brightness & contrast 0.2 with grayscale 0.5. As a result, the proposed modification achieved with 92.74% with brightness & contrast 0.2, 92.80% on brightness & contrast 0.2 with grayscale 0.5, and 92.86% on brightness & contrast 0.2 with grayscale 0.3. While grayscale 0.3 alone achieve 93.25% and grayscale 0.5 alone achieve 93.46% compared with the original results which reached 92.77%. © 2021 IEEE.

关键词： Luminance

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Machine Learning Enhanced Calculation of Quantum-Classical Binding Free Energies

arXiv

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arXiv 2025年

作者： Bensberg, Moritz Eckhoff, Marco Thomasen, F. Emil Bro-Jørgensen, William Teynor, Matthew S. Sora, Valentina Weymuth, Thomas Husistein, Raphael T. Knudsen, Frederik E. Krogh, Anders Lindorff-Larsen, Kresten Reiher, Markus Solomon, Gemma C. ETH Zurich Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 2 Zurich8093 Switzerland University of Copenhagen Department of Biology Linderstrøm-Lang Centre for Protein Science Ole Maaløes Vej 5 Copenhagen NDK-2200 Denmark University of Copenhagen Department of Chemistry and Nano-Science Center Universitetsparken 5 Copenhagen ØDK-2100 Denmark University of Copenhagen Niels Bohr Institute NNF Quantum Computing Programme Blegdamsvej 21 Copenhagen ØDK-2100 Denmark University of Copenhagen Department of Computer Science Universitetsparken 1 Copenhagen ØDK-2100 Denmark Center for Health Data Science Department of Public Health Denmark

Binding free energies are a key element in understanding and predicting the strength of protein–drug interactions. While classical free energy simulations yield good results for many purely organic ligands, drugs including transition metal atoms often require quantum chemical methods for an accurate description. We propose a general and automated workflow that samples the potential energy surface with hybrid quantum mechanics/molecular mechanics (QM/MM) calculations and trains a machine learning (ML) potential on the QM energies and forces to enable efficient alchemical free energy simulations. To represent systems including many different chemical elements efficiently and to account for the different description of QM and MM atoms, we propose an extension of element-embracing atom-centered symmetry functions for QM/MM data as an ML descriptor. The ML potential approach takes electrostatic embedding and long-range electrostatics into account. We demonstrate the applicability of the workflow on the well-studied protein–ligand complex of myeloid cell leukemia 1 and the inhibitor 19G and on the anti-cancer drug NKP1339 acting on the glucose-regulated protein 78. Copyright © 2025, The Authors. All rights reserved.

关键词： Ligands

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Deep and handcrafted feature supported diabetic retinopathy detection: A study

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Procedia computer science 2023年 218卷 2675-2683页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Chennai 600119 Tamil Nadu India

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes; (i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database.

关键词： Eye abnormality Diabetic retinopathy Fundus imaging Deep-Learning Classification

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

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Procedia computer science 2023年 218卷 2786-2794页

作者： Seifedine Kadry Enrique Herrera-Viedma Rubén González Crespo Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Granada Andalusian Spain Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Chennai 600119 Tamil Nadu India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases; (i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods.

关键词： Lung nodule CT scan CNN segmentation VGG-SegNet Accuracy Detection

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A generalization of Floater-Hormann interpolants

arXiv

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arXiv 2023年

作者： Themistoclakis, Woula Van Barel, Marc C.N.R. National Research Council of Italy IAC Institute for Applied Computing "Mauro Picone" via P. Castellino 111 Napoli80131 Italy KU Leuven Department of Computer Science KU Leuven Celestijnenlaan 200A Heverlee LeuvenB-3001 Belgium

In this paper the interpolating rational functions introduced by Floater and Hormann are generalized leading to a whole new family of rational functions depending on γ, an additional positive integer parameter. For γ = 1, the original Floater–Hormann interpolants are obtained. When γ > 1 we prove that the new rational functions share a lot of the nice properties of the original Floater–Hormann functions. Indeed, for any configuration of nodes in a compact interval, they have no real poles, interpolate the given data, preserve the polynomials up to a certain fixed degree, and have a barycentric-type representation. Moreover, we estimate the associated Lebesgue constants in terms of the minimum (h∗) and maximum (h) distance between two consecutive nodes. It turns out that, in contrast to the original Floater-Hormann interpolants, for all γ > 1 we get uniformly bounded Lebesgue constants in the case of equidistant and quasi-equidistant nodes configurations (i.e., when h ∼ h∗). For such configurations, as the number of nodes tends to infinity, we prove that the new interpolants (γ > 1) uniformly converge to the interpolated function f, for any continuous function f and all γ > 1. The same is not ensured by the original FH interpolants (γ = 1). Moreover, we provide uniform and pointwise estimates of the approximation error for functions having different degrees of smoothness. Numerical experiments illustrate the theoretical results and show a better error profile for less smooth functions compared to the original Floater-Hormann *** Codes 41A05 (Primary) 41A20, 41A25 (Secondary) Copyright © 2023, The Authors. All rights reserved.

关键词： Rational functions

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On the suitability of time series analysis on data warehouses

On the suitability of time series analysis on data warehouse...

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2007 IADIS European Conference on Data Mining, DM 2007, part of the 1st IADIS Multi Conference on computer science and Information Systems, MCCSIS 2007

作者： Zubcoff, Jose Trujillo, Juan Cuzzocrea, Alfredo Department of Sea Sciences and Applied Biology University of Alicante Spain Department of Software and Computing Systems University of Alicante Spain Department of Electronics Computer Science and Systems University of Calabria Italy

ISBN: (纸本)9789728924409

Nowadays, Time Series (TS) analysis is done more as an art rather than a science. It is frequently applied to flat files, obtained from external data sources such as operational databases. The main reason of this isolated data mining perspective is the lack of a methodology that integrates the conceptual modeling of the repositories, the pre-processing, and the posterior phases of the Knowledge Discovery in Databases (KDD) process. Such an isolated perspective of KDD can lead us to duplicate time-consuming tasks such as those of pre-processing steps. On the other hand, considering the main goals from the beginning of the knowledge discovery process allows us to improve data quality significantly. Previous work accomplishes the multidimensional model design of the Data Warehouse (DW). This work provides a conceptual model for the TS analysis on DW via establishing an abstract view on common features of this mining technique by reusing existing models of the underlying DW, thus achieving a comprehensive methodology allowing us to represent, map, and execute TS analysis tasks on DW. To show the suitability of our approach in a practical experimentation, we have mapped the conceptual models defined for a case study on platform specific models given by a commercial database system. As a result, hiding low-level details allows us to present the entire KDD process to users in terms of familiar concepts, thus facilitating the communication among human resources involved in the process. © 2007 IADIS

关键词： Data warehouses

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Data-driven approach for identifying the factors related to debt collector performance

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Journal of Open Innovation: Technology, Market, and Complexity 2024年第4期10卷

作者： Sivamayilvelan, Keerthana Rajasekar, Elakkiya Balachandran, Santhi Kotecha, Ketan Vairavasundaram, Subramaniyaswamy School of Computing SASTRA Deemed University Thanjavur 613401 India xPERT Research Group Department of Computer Science Birla Institute of Technology & Science Pilani - Dubai Campus Dubai 345055 United Arab Emirates School of Arts Sciences Humanities & Education (SASHE) SASTRA Deemed University Thanjavur 613401 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Pune India School of Computer Science and Engineering Vellore Institute of Technology Vellore 632014 India

The company's success and growth heavily rely on its workforce's performance, yet the evaluation of employees has been only partially and inconclusively executed so far. The primary goal of this research is to build an open innovation framework for analyzing the performance of the debt collector. We have developed the Reinforcement Learning based Continual Learning (RLC) approach for evaluating the performance by analyzing the metrics such as visit patterns and collection percentage. We have used the private debt collection dataset to assess the debt collector's performance. We formulated hypotheses derived from insights gained during exploratory data analysis and subsequently validated them through statistical testing. Whether there are noticeable distinctions among debt collectors in terms of visitation frequency, collection rates, and collection modes. This proposed open innovation framework for analyzing the debt collector performance provides significant variation in terms of collection rate. The proposed EDQN-CL achieved a 13.56 % higher classification rate than the existing algorithm for categorizing the debt collector performance. © 2024 The Authors

关键词： And machine learning Continual learning Debt collection Debt collector performance Deep reinforcement learning Statistical analysis

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Ad Hoc File Systems for High-Performance computing

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Journal of computer science & Technology 2020年第1期35卷 4-26页

作者： AndréBrinkmann Kathryn Mohror Weikuan Yu Philip Carns Toni Cortes Scott A.Klasky Alberto Miranda Franz-Josef Pfreundt Robert B.Ross Marc-AndréVef Zentrum für Datenverarbeitung Johannes Gutenberg University MainzMainz 55128Germany Center for Applied Scientific Computing Lawrence Livermore National LaboratoryLivermoreCA 94550U.S.A Department of Computer Science Florida State UniversityTallahasseeFL 32306U.S.A Mathematics and Computer Science Division Argonne National LaboratoryLemontIL 60439U.S.A Department of Computer Architecture Universitat Politecnica de CatalunyaBarcelona 08034Spain Computer Science and Mathematics Division Oak Ridge National LaboratoryOak RidgeTN 37831U.S.A Computer Science Department Barcelona Supercomputing CenterBarcelona 08034Spain Fraunhofer Institute for Industrial Mathematics ITWM Fraunhofer-Platz 1Kaiserslautern 67663Germany

Storage backends of parallel compute clusters are still based mostly on magnetic disks,while newer and faster storage technologies such as flash-based SSDs or non-volatile random access memory(NVRAM)are deployed within compute *** these new storage technologies into scientific workflows is unfortunately today a mostly manual task,and most scientists therefore do not take advantage of the faster storage *** approach to systematically include nodelocal SSDs or NVRAMs into scientific workflows is to deploy ad hoc file systems over a set of compute nodes,which serve as temporary storage systems for single applications or longer-running *** paper presents results from the Dagstuhl Seminar 17202"Challenges and Opportunities of User-Level File Systems for HPC"and discusses application scenarios as well as design strategies for ad hoc file systems using node-local storage *** discussion includes open research questions,such as how to couple ad hoc file systems with the batch scheduling environment and how to schedule stage-in and stage-out processes of data between the storage backend and the ad hoc file *** presented are strategies to build ad hoc file systems by using reusable components for networking and how to improve storage device *** interfaces and semantics are presented,for example those used by the three ad hoc file systems BeeOND,GekkoFS,and *** presentation covers a range from file systems running in production to cutting-edge research focusing on reaching the performance limits of the underlying devices.

关键词： parallel architectures distributed file system high-performance computing burst buffer POSIX(portable operating system interface)

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Carbon-Aware Edge computing for Internet of Everything Networks: A Digital Twin Approach

引用

IEEE Internet of Things Journal 2025年

作者： Huynh, Dang Van Khosravirad, Saeed R. Sharma, Vishal Kim, Joongheon Canberk, Berk Duong, Trung Q. Memorial University Faculty of Engineering and Applied Science St. John’sNLA1B 3X5 Canada Nokia Bell Labs Murray HillNJ07964 United States Queen’s University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Korea University Department of Electrical and Computer Engineering Seoul02841 Korea Republic of Engineering and The Built Environment at Edinburgh Napier University School of Computing EdinburghEH11 4BN United Kingdom

The rapid growth of edge computing has enabled low-latency and high-efficiency processing for a wide range of applications;however, it also leads to significant energy consumption and carbon emissions. In this context, this study investigates a CO2 emission minimisation problem in a digital twin-aided edge computing system, aiming to optimise task offloading decisions, transmit power, and processing rates of Internet of Things (IoT) devices. To address the formulated mixed-integer non-linear programming problem, we propose two solutions: an alternating optimisation method based on the successive convex approximation framework and a deep reinforcement learning (DRL) approach. Extensive simulations validate the effectiveness of the proposed solutions, demonstrating significant reductions in CO2 emissions, robust optimisation performance, and superior results compared to benchmark schemes. The findings highlight the feasibility of integrating advanced optimisation and artificial intelligence-driven techniques to achieve environmentally sustainable and high-performance edge computing systems, paving the way for greener technological innovation. © 2014 IEEE.

关键词： Deep reinforcement learning

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State-space models are accurate and efficient neural operators for dynamical systems

arXiv

引用

arXiv 2024年

作者： Hu, Zheyuan Daryakenari, Nazanin Ahmadi Shen, Qianli Kawaguchi, Kenji Em Karniadakis, George Department of Computer Science National University of Singapore Singapore119077 Singapore Center for Biomedical Engineering School of Engineering Brown University ProvidenceRI02912 United States Division of Applied Mathematics Brown University ProvidenceRI02912 United States Advanced Computing Mathematics and Data Division Pacific Northwest National Laboratory RichlandWA United States

Physics-informed machine learning (PIML) has emerged as a promising alternative to classical methods for predicting dynamical systems, offering faster and more generalizable solutions. However, existing models, including recurrent neural networks (RNNs), transformers, and neural operators, face challenges such as long-time integration, long-range dependencies, chaotic dynamics, and extrapolation, to name a few. To this end, this paper introduces state-space models implemented in Mamba for accurate and efficient dynamical system operator learning. Mamba addresses the limitations of existing architectures by dynamically capturing long-range dependencies and enhancing computational efficiency through reparameterization techniques. To extensively test Mamba and compare against another 11 baselines, we introduce several strict extrapolation testbeds that go beyond the standard interpolation benchmarks. We demonstrate Mamba’s superior performance in both interpolation and challenging extrapolation tasks. Mamba consistently ranks among the top models while maintaining the lowest computational cost and exceptional extrapolation capabilities. Moreover, we demonstrate the good performance of Mamba for a real-world application in quantitative systems pharmacology for assessing the efficacy of drugs in tumor growth under limited data scenarios. Taken together, our findings highlight Mamba’s potential as a powerful tool for advancing scientific machine learning in dynamical systems modeling. (The code will be available at https://***/zheyuanhu01/State_ Space_Model_Neural_Operator upon acceptance.) © 2024, CC BY.

关键词： Recurrent neural networks

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